






# Algorithm for the pigeonhole sorting








def 					lowerCamelCase__					(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			min(_UpperCAmelCase)  # min() finds the minimum value
			SCREAMING_SNAKE_CASE							=			max(_UpperCAmelCase)  # max() finds the maximum value

			SCREAMING_SNAKE_CASE							=			max_val - min_val + 1  # size is difference of max and min values plus one

			# list of pigeonholes of size equal to the variable size
			SCREAMING_SNAKE_CASE							=			[0] * size

			# Populate the pigeonholes.
			for x in a:
						assert isinstance(_UpperCAmelCase				,							_UpperCAmelCase), "integers only please"
						holes[x - min_val] += 1

			# Putting the elements back into the array in an order.
			SCREAMING_SNAKE_CASE							=			0
			for count in range(_UpperCAmelCase):
						while holes[count] > 0:
									holes[count] -= 1
									SCREAMING_SNAKE_CASE							=			count + min_val
									i += 1






def 					lowerCamelCase__					():
			SCREAMING_SNAKE_CASE							=			[8, 3, 2, 7, 4, 6, 8]
			pigeonhole_sort(_UpperCAmelCase)
			print('Sorted order is:'				,							' '.join(_UpperCAmelCase))


if __name__ == "__main__":
				main()












"""simple docstring"""





import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMSNForImageClassification, ViTMSNModel
  from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class 	a		:






    def __init__(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str=13			,		__SCREAMING_SNAKE_CASE	:				int=30			,		__SCREAMING_SNAKE_CASE	:				List[Any]=2			,		__SCREAMING_SNAKE_CASE	:				Dict=3			,		__SCREAMING_SNAKE_CASE	:				List[str]=True			,		__SCREAMING_SNAKE_CASE	:				str=True			,		__SCREAMING_SNAKE_CASE	:				Any=32			,		__SCREAMING_SNAKE_CASE	:				List[str]=5			,		__SCREAMING_SNAKE_CASE	:				List[str]=4			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=37			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]="gelu"			,		__SCREAMING_SNAKE_CASE	:				List[Any]=0.1			,		__SCREAMING_SNAKE_CASE	:				Tuple=0.1			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=10			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.02			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=None			,		)	->						List[str]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		patch_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		hidden_size
      lowerCamelCase_       	=		num_hidden_layers
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		intermediate_size
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		hidden_dropout_prob
      lowerCamelCase_       	=		attention_probs_dropout_prob
      lowerCamelCase_       	=		type_sequence_label_size
      lowerCamelCase_       	=		initializer_range
      lowerCamelCase_       	=		scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      lowerCamelCase_       	=		(image_size // patch_size) ** 2
      lowerCamelCase_       	=		num_patches + 1






    def   UpperCamelCase			(			self	:				Dict   )	->						Union[str, Any]:
      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      lowerCamelCase_       	=		None
      if self.use_labels:
        lowerCamelCase_       	=		ids_tensor([self.batch_size]			,		self.type_sequence_label_size   )

      lowerCamelCase_       	=		self.get_config()

      return config, pixel_values, labels






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Union[str, Any]:
      return ViTMSNConfig(
          image_size=self.image_size			,		patch_size=self.patch_size			,		num_channels=self.num_channels			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		initializer_range=self.initializer_range			,		)






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Any:
      lowerCamelCase_       	=		ViTMSNModel(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				str   )	->						List[str]:
      lowerCamelCase_       	=		self.type_sequence_label_size
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		labels=__SCREAMING_SNAKE_CASE   )
      print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'   )
      print('Labels: {labels}'   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )

      # test greyscale images
      lowerCamelCase_       	=		1
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()

      lowerCamelCase_       	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						Tuple:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 	a		(   __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	str		=					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE		:	Dict		=					(
        {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE		:	Dict		=					False
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					False
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					False
    SCREAMING_SNAKE_CASE		:	str		=					False






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[str]:
      lowerCamelCase_       	=		ViTMSNModelTester(self   )
      lowerCamelCase_       	=		ConfigTester(self			,		config_class=__SCREAMING_SNAKE_CASE			,		has_text_modality=__SCREAMING_SNAKE_CASE			,		hidden_size=37   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      self.config_tester.run_common_tests()






    @unittest.skip(reason='ViTMSN does not use inputs_embeds'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						Union[str, Any]:
      pass






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[Any]:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        self.assertIsInstance(model.get_input_embeddings()			,		(nn.Module)   )
        lowerCamelCase_       	=		model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE			,		nn.Linear   )   )






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase_       	=		[*signature.parameters.keys()]

        lowerCamelCase_       	=		['pixel_values']
        self.assertListEqual(arg_names[:1]			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[int]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase_       	=		ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				) ->							List[Any]:
  lowerCamelCase_       	=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
  return image






@require_torch
@require_vision
class 	a		(   unittest.TestCase						):






    @cached_property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'   ) if is_vision_available() else None






    @slow
    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      torch.manual_seed(2   )
      lowerCamelCase_       	=		ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'   ).to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.default_image_processor
      lowerCamelCase_       	=		prepare_img()
      lowerCamelCase_       	=		image_processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   ).to(__SCREAMING_SNAKE_CASE   )

      # forward pass
      with torch.no_grad():
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )

      # verify the logits
      lowerCamelCase_       	=		torch.Size((1, 1000)   )
      self.assertEqual(outputs.logits.shape			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.tensor([-0.0_803, -0.4_454, -0.2_375]   ).to(__SCREAMING_SNAKE_CASE   )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		__SCREAMING_SNAKE_CASE			,		atol=1e-4   )   )




